It has become increasingly commonplace to use experiment designs as a tool to derive models of complex systems in an effort to identify inputs (commonly referred to as “factors”) that explain observed outputs (commonly referred to as “responses”), especially where there is a need to change undesired responses. However, the derivation of a model that provides an understanding of a complex system that is sufficient to explain a linkage between particular factors and particular responses is often a time-consuming task, since each particular type of model is typically closely associated with a particular type of experiment design. Thus, it is often necessary to suffer through a wasteful trial-and-error process in which best efforts to select a type of model that is believed to be capable of providing such a sufficient understanding of a system leads to a choice of experiment design that is later found to be undesirably ineffective in illuminating a linkage between particular factors and particular responses. Thus, there may be multiple iterations of selection of a type of model followed by the revelation of the need to make another selection only after an expenditure of considerable time to perform the associated type of experiment design.
Even after the identification of a type of model and associated type of experiment design that at least appears to be sufficiently capable of illuminating a linkage between particular factors and responses, additional considerable time may be consumed in iteratively deriving coefficients of the model and/or other parameters of the associated experiment design to derive a sufficiently useful model. Also, practical limitations of cost, availability of materials and/or available time may impose the need to perform the associated experiment design in a less than technically ideal manner, and such impositions may need to be taken into account in deriving the model.